This replication archive provides all data necessary to replicate:

	Patrick Rogers. 2008. "Learning to do Better: Using Boosting Neural
	Networks and Leader Experience Measures to Improve the Accuracy of
	Conflict Prediction Models" Unpublished.

This archive contains the following files:

dpost.dta:  The original "Beck and Tucker, MWPSA 1998" dataset that has
            info on the variables; (in Stata format)
leader.dta: Dataset adapted from Bruce Bueno de Mesquita, B. and Smith, A.
            and Siverson, R.M. and Morrow, J.D. 2003. The Logic of
            Political Survival. MIT Press: Cambridge, MA.
boost.dta:  The actual dataset used for the analysis presented in the
            paper.
dpost.do:   The stata command file that produces boost.dta from dpost.dta
            and leader.dta. Appends tenure data for each state and
            randomizes the assignment dema and demb and saves the right
            variables.
boost.R:    R command file that runs the analysis presented in the paper.
            The boost.dta file is loaded. A simulation is run to determine
            the optimal number of hidden layer nodes, and the optimal decay
            rate (the analyses presented in the paper found that 22 hidden
            nodes and a decay rate of .01 was optimal). Then four analyses
            are run: A single neural network is trained and then tested
            against the data. An ensemble committee of 10 independent
            neural networks are tested against the data. A boosted neural
            network consisting of three learners is tested against the
            data, and finally an ensemble committee of 10 boosted neural
            networks are tested against the data.

It should be noted that some of the series used in this data set are updated from time to time. Thus exact replication of our results requires the use of these exact data sets. The updates of the series we use are inconsequential.